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2nd IEEE International Conference on Intelligent Technologies, CONIT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2029213


Medical Image Segmentation has an imperative job in diagnostic systems on various applications. Ultrasounds, X-rays, MRI, CAT and PET (Positron Emission Tomography) are dynamic and developing domains for research especially in image-processing techniques and algorithms. This field has also attracted significant investments and developments in recent times. Deep Learning models, specifically the Convolutional Neural Network Models (CNN) are state-of-art technologies for identifying medical ailments through visual imagery. The objective of this research is to develop and implement a DepthWise Convolution model that provides high accuracy in detecting Covid 19 Pneumonia from lung x-rays. We also juxtapose it with other models which have great accuracy i.e Transfer Learning Models. © 2022 IEEE.

International Journal of Current Research and Review ; 13(3):113-119, 2021.
Article in English | Scopus | ID: covidwho-1083469


Introduction: A novel threat to mankind occurred in December 2019 which was an outbreak of infection caused by a novel coronavirus (SARS-CoV-2 or 2019-nCoV). The infection was first developed in Wuhan, China, and has affected more than 200 countries around the world till now. Objective: The present study aims to assess the knowledge related to coronavirus disease (COVID-19), risk perception and preventive behaviours among the Pharmacy students in a part of India approximately 3 months after the onset of this outbreak in India. Methods: This survey was conducted from 2nd to 5th of September 2020 with Indian Pharmacy students (1st to 4th year). The knowledge, self-reported preventive behaviours and risk perceptions of COVID-19 were assessed using an online questionnaire. A total of 21 questions were there in the questionnaire in which 14 questions were about knowledge related to COVID-19, 4 items regarding preventive behaviours and 3 about risk perception. Results: A total of 268 participants completed the questionnaire. The participants were under the age group of 15-30 years. A high level of disease-related knowledge was found in the participants (77.66%). On an average 96.1% of participants were practising preventive behaviours. The aggregate score of items in risk perception section was found to be in the moderate range i.e., 5.38 out of 8. A significant negative correlation was obtained between risk perception and preventive behaviours. Conclusion: The trajectory and severity of this outbreak are very high, therefore, effective treatment against this global threat is required to be developed as early as possible. In the present study, a high level of disease-related knowledge and preventive behaviours were observed among the participants with a moderate level of risk perception. © IJCRR.